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In this paper we present a new occlusion handling technique which successfully addresses the intricate problem of extraction of occluded features for urban landscape analysis and cartography. This new method is based on temporal integration in which multiple sessions or passages are used to complete occluded features in a 3D cartographic image. 3D image obtained from each passage is first characterized and classified into three main object classes: Permanently static, Temporarily static and Mobile using inference based on basic reasoning and a new point matching technique, intelligently exploiting the different viewing angles of the mounted Lidar sensors. All the Temporarily static and Mobile objects, considered as occluding objects, are removed from the image/scene leaving behind a perforated 3D image of the cartography. This perforated image is then updated by similar subsequent perforated images, obtained on different days and hours of the day, filling in the holes and completing the missing features of the urban cartography. This ensures that the resulting 3D image of the cartography is most accurate containing only the exact and actual permanent features. Separate update and reset functions are specially added to increase robustness of the method. The proposed method is evaluated on a standard data set demonstrating its efficacy and suitability for different applications.